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About the course
Fear of disruption. It's a common feeling across today's business landscape: fear that a more agile company will swoop in with a pure-play digital business model, leveraging data in ways that compel competitive advantage.
Big data is facilitated by technology, but it’s optimized by people, culture, and processes. Investment in big data and related technologies is at an all-time high, yet according to research by New Vantage Partners, less than half of the companies say they are competing on data and analytics (48 percent); only 31 percent have created a data-driven organization, and only 28 percent have forged a data culture. The challenge in realizing the potential of big data lies not in the technology itself, but rather in transforming people, culture, and processes.
Berkeley’s Data Science: Bridging Principles and Practice gives participants with non-technical backgrounds a foundational understanding of what data science and analytics is all about, and many of the most common techniques used to manipulate and analyze data yourself. At the end of this program, you should be able not only to work effectively with data science and analytics teams by speaking their language, but also help guide them to deliver business value to you and the entire organization.
Adapt to a Data Mindset
Learn techniques for applying data to answer specific business questions and insights
Learn the Ability to Communicate and Interpret Data
Gain tools to effectively communicate with data scientists and learn how to interpret and present their data
Create a Data-Driven Culture
Learn how to create a culture within your organization that is data-driven and the capabilities that make data science teams successful
The Data Science: Bridging Principles and Practice program curriculum covers the following topics:
MODULE 1 | Foundations of Data Science
MODULE 2 | The Science of Surveys for Business Decisions
MODULE 3 | Hypothesis Testing for Business Decisions
MODULE 4 | Extrapolating Information from Sample Data
MODULE 5 | Regression Analysis for Business Decisions
MODULE 6 | Forecasting and A/B Testing
MODULE 7 | Machine Learning
MODULE 8 | Building Effective Data Science Teams
- World-renowned Berkeley Haas and Executive Education Faculty
- Video lectures from Berkeley Haas faculty
- Live, interactive webinars (also available as recordings)
- Peer discussions and exercises
- Case Studies
- Exclusive online network-building opportunities
- Access to the program alumni community
- Data Science: Bridging Principles and Practice Certificate of Completion
Who should attend
This program is for individual contributors and mid-level to senior managers from either the private or public sectors seeking a truly rigorous, hands-on experience with modern data analysis methods.
Representative roles and industries that can benefit include:
- Managers who manage or will manage data science teams or vendors
- Performance marketing professionals
- Product Engineers, Product Managers, and R&D Managers
- Business/technology strategists and consultants
- Human Resources professionals
- Technology-driven industries where data analysis is critical including retail, information technology, e-commerce, financial services, fintech, manufacturing and healthcare
Trust the experts
Education Ph.D., Economics, New York University M.A., Economics, New York University B.A., Economics, Tel Aviv University Positions Held At Haas since 2009 2009 – present, Lecturer, Haas School of Business 2014 – present, Department Chair, UC Berkeley, Department of Economics 2014 – ...
Steve Tadelis is the James J. and Marianne B. Lowrey Chair in Business and a Professor of Economics, Business and Public Policy at Haas School of Business, University of California, Berkeley. Steve was the Joe Shoong Chair in International Business (2015-2016) and the Associate Dean for Strategic...
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